TY - GEN
T1 - Distribution transformer oil age prediction using neuro wavelet
AU - Setiawati, Novie Elok
AU - Rosmaliati,
AU - Lystianingrum, Vita
AU - Priyadi, Ardyono
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/13
Y1 - 2018/11/13
N2 - The distribution transformer is one of the vital components in the power system distribution, which deliver electricity power to the consumer. Various disturbances on the transformers can cause a decrease of their performance, so that they cannot reach the operation life. This study proposes a simulation study to predict the transformer oil age by using wavelet transform and backpropagation neural network. Transformer's current measurement was carried out in North Surabaya with a rating of 20 KV/380-220V and capacity of $100~\mathrm {k}\mathrm {V}\mathrm {A}$. The secondary current of the distribution transformer has been processed using the haar wavelet to obtain the detail coefficients, which is used to calculate the energy and PSD (power spectral density) value. Energy value and PSD are the input data on training and testing of back propagation neural network, while the output (target) is the transformer oil age. The simulation results show that the proposed method can predict the transformer oil age with an accuracy rate of 89.5795%.
AB - The distribution transformer is one of the vital components in the power system distribution, which deliver electricity power to the consumer. Various disturbances on the transformers can cause a decrease of their performance, so that they cannot reach the operation life. This study proposes a simulation study to predict the transformer oil age by using wavelet transform and backpropagation neural network. Transformer's current measurement was carried out in North Surabaya with a rating of 20 KV/380-220V and capacity of $100~\mathrm {k}\mathrm {V}\mathrm {A}$. The secondary current of the distribution transformer has been processed using the haar wavelet to obtain the detail coefficients, which is used to calculate the energy and PSD (power spectral density) value. Energy value and PSD are the input data on training and testing of back propagation neural network, while the output (target) is the transformer oil age. The simulation results show that the proposed method can predict the transformer oil age with an accuracy rate of 89.5795%.
KW - Backpropagation neural network
KW - Distribution transformer
KW - Energy value
KW - Haar wavelet
KW - PSD
UR - https://www.scopus.com/pages/publications/85058387981
U2 - 10.1109/ICITEED.2018.8534830
DO - 10.1109/ICITEED.2018.8534830
M3 - Conference contribution
AN - SCOPUS:85058387981
T3 - Proceedings of 2018 10th International Conference on Information Technology and Electrical Engineering: Smart Technology for Better Society, ICITEE 2018
SP - 202
EP - 207
BT - Proceedings of 2018 10th International Conference on Information Technology and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Information Technology and Electrical Engineering, ICITEE 2018
Y2 - 24 July 2018 through 26 July 2018
ER -